This paper explores the effectiveness of data augmentation in image classification using deep learning. The authors compare various data augmentation techniques, including traditional transformations, GANs, and neural augmentation. They evaluate these methods on two datasets: tiny-imagenet-200 and MNIST. The goal is to determine which augmentation strategies improve classification accuracy, reduce overfitting, and help models converge faster. Traditional data augmentation techniques such as cropping, rotating, and flipping are effective. However, the authors also experiment with GANs to generate images of different styles. They propose a novel method called neural augmentation, where a neural network learns to generate augmentations that best improve the classifier. This method involves training a neural network to both augment and classify images simultaneously. The authors evaluate the performance of these techniques on two datasets: tiny-imagenet-200 (dogs vs. cats, dogs vs. goldfish) and MNIST (distinguishing 0s from 8s). The results show that neural augmentation performs significantly better than traditional augmentation and no augmentation. In the dogs vs. cats problem, neural augmentation achieved a 91.5% accuracy compared to 85.5% with no augmentation. In the dogs vs. fish problem, neural augmentation achieved 77.0% accuracy compared to 70.5% with no augmentation. The authors also note that neural augmentation has no effect on the MNIST dataset, as a simple CNN already performs well on this structured data. They hypothesize that the digits are so simple that combining features does not add additional information. The results show that neural augmentation helps reduce overfitting and improves classification accuracy. However, the authors note that traditional augmentation is also effective and can be combined with neural augmentation for better results. The study concludes that data augmentation is a promising technique for improving classification accuracy, especially in scenarios with limited data. The authors suggest that future work should explore more complex architectures and more varied datasets, as well as the application of these techniques to videos and other domains.This paper explores the effectiveness of data augmentation in image classification using deep learning. The authors compare various data augmentation techniques, including traditional transformations, GANs, and neural augmentation. They evaluate these methods on two datasets: tiny-imagenet-200 and MNIST. The goal is to determine which augmentation strategies improve classification accuracy, reduce overfitting, and help models converge faster. Traditional data augmentation techniques such as cropping, rotating, and flipping are effective. However, the authors also experiment with GANs to generate images of different styles. They propose a novel method called neural augmentation, where a neural network learns to generate augmentations that best improve the classifier. This method involves training a neural network to both augment and classify images simultaneously. The authors evaluate the performance of these techniques on two datasets: tiny-imagenet-200 (dogs vs. cats, dogs vs. goldfish) and MNIST (distinguishing 0s from 8s). The results show that neural augmentation performs significantly better than traditional augmentation and no augmentation. In the dogs vs. cats problem, neural augmentation achieved a 91.5% accuracy compared to 85.5% with no augmentation. In the dogs vs. fish problem, neural augmentation achieved 77.0% accuracy compared to 70.5% with no augmentation. The authors also note that neural augmentation has no effect on the MNIST dataset, as a simple CNN already performs well on this structured data. They hypothesize that the digits are so simple that combining features does not add additional information. The results show that neural augmentation helps reduce overfitting and improves classification accuracy. However, the authors note that traditional augmentation is also effective and can be combined with neural augmentation for better results. The study concludes that data augmentation is a promising technique for improving classification accuracy, especially in scenarios with limited data. The authors suggest that future work should explore more complex architectures and more varied datasets, as well as the application of these techniques to videos and other domains.